tbuckley/cabot-search huggingface dataset:
Abstract 5643: High-spatial-resolution multi-omics analysis of cancer tissues
Authors: Emily Neil, Dongju Park, Erica Lloyd, Michael Dibuono, Seiyu Hosono, Reto Muller, Hanna Lafayette, Hsinyi Smith, Robert Pinard
Journal: Cancer Research (2023)

Abstract Recent technological breakthroughs in single cell sequencing have revealed that malignant cells and nonmalignant cells are both highly dynamic and in the case of tumor cells, that the intratumoral heterogeneity is quite remarkable. However, several questions remain, especially in a spatial context, and a rapidly growing need has been observed for imaging-based approaches with single-cell resolution to visualize and characterize the tumor microenvironment. In the current study, we used Miltenyi Biotec newly developed spatial gene expression technology and automated imaging platform, to acquire spatial microscopy data for multiple biological analytes and multiple cancer tissues. The approach is based on fluorescence microscopy and the MICS technology principle of cyclic staining (MACSima™ Imaging Cyclic Staining) along with validated fluorochrome-conjugated antibodies and specifically amplified oligonucleotide probes combined with a proprietary signal removal system. FFPE tissue blocks were analyzed and each gene, which display a unique emission spectrum during a single cycle, was decoded. Due to the non-destructive nature of the workflow, H&amp;E staining, immunofluorescence and RNA data can be collected together into a single image and on a single section. Using our antibody and targeted RNA immune-oncology panel coupled with our smart segmentation and flexible gating software suite, we have generated a topographical map of the expression profile level for several cancer tissues i.e.: tonsil, pancreas, kidney and ovary. This technology allows for the analysis of hundreds of protein markers and RNA transcripts on a single tissue, the characterization of the spatial architecture and the expression topography of tumor tissue sections. A better understanding of tissue microenvironment and implementation of spatial biology approaches will help advance the identification of more suitable biomarkers and for cancer therapy. Citation Format: Emily Neil, Dongju Park, Erica Lloyd, Michael Dibuono, Seiyu Hosono, Reto Muller, Hanna Lafayette, Hsinyi Smith, Robert Pinard. High-spatial-resolution multi-omics analysis of cancer tissues. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5643.
Abstract 2087: <i>Sherlock-Genome</i>: a R shiny app for genomic analysis and visualization
Authors: Alyssa Klein, Maria Teresa Landi, Tongwu Zhang
Journal: Cancer Research (2023)

Abstract Over the last few decades, next generation sequencing technologies, including whole genome sequencing (WGS), have been increasingly used in cancer genomics studies. However, there is no efficient way to manage, visualize and integratively analyze sample-level WGS results across different bioinformatic pipelines, particularly for non-bioinformaticians. This greatly limits the usability of WGS data for biological discovery. Following the FAIR principles (findability, accessibility, interoperability, and reusability), we developed Sherlock-Genome, a R Shiny app designed for data harmonization, visualization and integrative analyses for WGS-based cancer genomics studies. Specifically, Sherlock-Genome provides pipelines for WGS analyses coupled with detailed guidelines on data preparation. The users can download the pipelines on their local computer, run the analyses and then upload the sample-level results into the Sherlock-Genome for data harmonization and integrative analyses across different genomic features. Sherlock-Genome also implements rigorous WGS-related data QC steps based on NGSpurity, a workflow that estimates the tumor purity, ploidy and clonal architecture by integrating somatic copy number alterations (SCNA), single nucleotide variants (SNV) and cancer cell fraction. Currently, Sherlock-Genome includes a few independent modules that allow users to generate a variety of interactive data inspection and visualization images, and perform many integrative analyses also using clinical and epidemiological data. These modules include study information and sample-level data QC (study overview, manifest information, NGSpurity), summaries of major genomics alterations (mutations, SCNA, structural variants), reports of advanced genomics analyses (mutational signatures, genomic landscape, clonal evolution) and functions related to different types of integrative analyses (survival analysis, integrative analysis). In addition, all plots in the Sherlock-Genome app can be downloaded as publication-ready figures from each module. Sherlock-Genome can be deployed in both local and cloud environments, allowing users to share sample-level analytical results together with the related publications. Sherlock-Genome has the potential to be widely used in cancer genomics allowing sample-level integrative analyses across multiple WGS and additional multi-omics features. A Sherlock-Genome demo with major module functions could be presented to allow users to know about this new interactive user-friendly tool for data analysis and visualization that can greatly advance biological discoveries. Citation Format: Alyssa Klein, Maria Teresa Landi, Tongwu Zhang. Sherlock-Genome: a R shiny app for genomic analysis and visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2087.
Abstract 1491: Delineating the oncogenic mechanisms of NAT10 in hepatocellular carcinoma
Authors: Emmely A. Patrasso, Sweta P. Raikundalia, Daniel Arango
Journal: Cancer Research (2023)

Abstract Protein synthesis is often dysregulated in cancer, highlighting the regulatory mechanisms of translation as potential targets for cancer treatment. With more than 140 chemical modifications of RNA, the epitranscriptome is a central mechanism in regulating protein synthesis. Remarkably, the epitranscriptome is often altered in cancerous tumors and under stress conditions such as reactive oxygen species (ROS) and anti-tumor therapies. For instance, N-acetyltransferase 10 (NAT10), the enzyme responsible for acetylating cytidines (N4-acetylcytidine or ac4C) in RNA, is highly overexpressed in cancers such as Hepatocellular Carcinoma (HCC). Of significant relevance to cancer, NAT10 is considered an oncogenic driver in fatty acid-induced HCC. Hence, this project aims to characterize the underlying oncogenic mechanisms of the NAT10/ac4C axis. Our data indicate that NAT10 and RNA acetylation levels are altered in response to fatty acids, ROS, and chemotherapeutic drugs. Likewise, we observed altered subcellular localization of NAT10 and ac4C in response to ROS and chemotherapeutic drugs. Building on our previous findings that NAT10-catalyzed ac4C promotes translation efficiency, our model stipulates that subcellular redistribution of the RNA acetyltransferase complexes enhances cell cycle progression, cancer cell proliferation, and cellular resilience by fine-tuning the translation of cancer driver genes. Collectively, our findings advance our understanding of how NAT10 promotes cancer growth while providing the mechanistic basis to target NAT10 for therapeutic purposes. Citation Format: Emmely A. Patrasso, Sweta P. Raikundalia, Daniel Arango. Delineating the oncogenic mechanisms of NAT10 in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1491.
Abstract 3954: Evaluation of long mononucleotide repeat markers for detection of microsatellite instability
Authors: Suping Chen, John H. Lin, Aparna Pallavajjala, Tamara L. Lotan, Jeffery W. Bacher, James R. Eshleman
Journal: Cancer Research (2023)

Abstract Microsatellite instability (MSI) and mismatch repair immunohistochemistry are biomarkers of defective MMR (dMMR) that predict response to immune checkpoint inhibitor therapy in solid tumors. The microsatellites in the Promega MSI Analysis System Version 1.2 may not be ideal to detect MSI in certain cancer types. Long mononucleotide repeat (LMR) markers may improve detection of microsatellite instability in early colorectal lesions and non-colorectal cancers.In this study, we compared the performance of the current gold standard, MSI Analysis System Version 1.2 (Promega), which consists of 5 mononucleotide repeat markers (21-27 bases), with a new LMR kit (Promega), which consists of 4 markers from the MSI V1.2 panel and 4 LMR loci (52-60 bases). We studied five cohorts including 24 MMR proficient (pMMR) and 24 dMMR colorectal cancer (CRC) samples, 24 pMMR and 42 dMMR endometrial cancer (EC) samples, 12 dMMR prostate cancer (PC) samples, 22 MSI-high (MSI-H) samples of other cancer types, and 12 MSI-low (MSI-L) samples, where MMR status was confirmed by immunohistochemical (IHC) staining and/or MSI Analysis System Version 1.2.The specificity and sensitivity of the LMR MSI panel for dMMR detection were both 100% in CRC. The specificity of the MSI V1.2 and LMR MSI panels in EC was both 100%, and the sensitivity was 88% versus 98%, respectively. The 22 MSI-high (MSI-H) samples of other cancer types, these include cholangiocarcinoma, appendix, duodenal, pancreatic and gastric cancer, which were previously classified as MSI-H by using the MSI V1.2 panel were also classified as MSI-H by using the LMR MSI panel. Among 12 samples that were previously classified as MSI-L by the MSI V1.2 panel, 9 of the samples were classified as MSI-L, and 3 of the samples were diagnosed as MSI-H using the MSI LMR panel. The LMR panel has performed well on 80 samples over the past 6 months.The LMR MSI panel is highly concordant with the MSI V1.2 panel for dMMR detection in colorectal cancer and showed increased sensitivity in endometrial cancer. The LMR MSI panel showed improved dMMR detection in non-colorectal cancers. Citation Format: Suping Chen, John H. Lin, Aparna Pallavajjala, Tamara L. Lotan, Jeffery W. Bacher, James R. Eshleman. Evaluation of long mononucleotide repeat markers for detection of microsatellite instability. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3954.
Abstract 3230: Tumor microenvironment modulation by immunotherapy sensitizes solid tumors to radiation
Authors: Minjeong Kang, DaeYong Lee, Yifan Wang, Betty Kim, Wen Jiang
Journal: Cancer Research (2023)

Abstract Radiation therapy (RT) has been prevalently implemented to treat cancer in clinical settings by inducing DNA damage in tumor cells. However, tumor microenvironmental factors such as severe hypoxic conditions and vascular anomalies attenuate the therapeutic efficacy of RT, thus resulting in unsatisfactory outcomes in solid tumors. Recently, Immune checkpoint blockade (ICB) has improved immune recognition to tumor cells and normalized tumor vasculature to diminish hypoxia in solid tumors. Herein, we suggest that RT combined with ICB synergistically alters tumor microenvironments, thus facilitating the recruitment of cytotoxic immune cells into tumors as well as sensitizing radiation effectiveness. RT in combination with ICB not only normalized tumor vasculature to reduce hypoxia, but also increased anti-tumor effects from immune-mediated toxicity and DNA damage-mediated apoptosis in several solid tumor-bearing mouse models to a greater extent than did either monotreatment of RT or ICB. This study will provide mechanistic insight into synergistic effects conferred by RT and ICB as well as a strong preclinical rationale for combining the two therapeutic modalities in human cancers. Citation Format: Minjeong Kang, DaeYong Lee, Yifan Wang, Betty Kim, Wen Jiang. Tumor microenvironment modulation by immunotherapy sensitizes solid tumors to radiation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3230.
Abstract 2323: ZGGS18, a bispecific drug candidate of anti-VEGF and TGFbeta-trap, inhibiting tumor proliferation and improving tumor microenvironment with I/O therapy
Authors: Ruifeng Liu, Alfonso Suarez, Tyler Liban, Bin Zhang, Tongcheng Dai, Margaret Karow, Jackie Sheng, Zelin Sheng, Binhua Lv, Bing Zhu
Journal: Cancer Research (2023)

Abstract ZGGS18 is a bifunctional fusion protein targeting both human vascular endothelial growth factor (VEGF) and transforming growth factor-β (TGF-β), which consist of a monoclonal antibody against VEGF and an engineered extracellular domain of TGF-β receptor II (TGF-βRII ECD). In vitro studies show ZGGS18 specifically and simultaneously binding towards various VEGFs and capturing different TGF-β isoforms by trap domains that play a synergistic role to block signaling of VEGF/TGF-β with their receptors. For instance, ZGGS18 inhibits human VEGF121 and 165 induced HUVEC proliferation; ZGGS18 also neutralizes TGF-β1 and -β3 related growth arrest of HT-2 cells. Further studies show that ZGGS18 does not induce ADCC- or CDC-dependent killing tumor cells expressing two soluble targets, suggesting its binding affinities towards Fc-gamma and complement C1q receptors without effector functions. These results support functional depletion of VEGF/TGF-β in tumor microenvironment (TME) by ZGGS18 essentially for its MOA to inhibit tumor growth. A dose-dependent in vivo anti-tumor efficacy of ZGGS18 (1, 3 and 15 mg/kg) has been found in PBMC humanized H460 lung cancer xenograft model. In addition, ZGGS18 has enhanced efficacies to kill tumor growth in MC38 xenograft models when it is combined with anti-PD-1 antibodies (keytruda), support the improvement of TME for I/O reagents by ZGGS18 in cooperation with its role in anti-proliferation and anti-angiogenesis. Toxicity and safety pharmacology studies with weekly repeat doses for four weeks in cynomolgus monkeys indicate that ZGGS18 is well tolerated over the dose range 13, 40 and 120 mg/kg without obviously toxic or adverse effects (NOAEL =120 mg/kg). PK/TK analyses indicate that ZGG18 has a prolonged half-life in plasma, attribute to its human neonatal Fc receptor (FcRn) binding and pH-dependent interaction for mAb recycling. The IND application of ZGGS18 has been approved by both FDA and Chinese NMPA for clinical trials in treatment of advanced tumors as monotherapy and/or in combined with PD-1/PD-L1 based I/O therapies. Citation Format: Ruifeng Liu, Alfonso Suarez, Tyler Liban, Bin Zhang, Tongcheng Dai, Margaret Karow, Jackie Sheng, Zelin Sheng, Binhua Lv, Bing Zhu. ZGGS18, a bispecific drug candidate of anti-VEGF and TGFbeta-trap, inhibiting tumor proliferation and improving tumor microenvironment with I/O therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2323.
Abstract 4072: Class I HLA-independent lysis of immunotherapy-resistant melanoma by CD8 T cells
Authors: Hongyan Xie, Aiping Jiang, Anne Jenney, Yi Sun, Tatyana Sharova, Moshe Sade-Feldman, Or-yam Revach, Angelina Cicerchia, Martin Q. Rasmussen, Nir Hacohen, Robert T. Manguso, Russell W. Jenkins
Journal: Cancer Research (2023)

Abstract Cancer immunotherapy with immune checkpoint blockade (ICB) has transformed the treatment of melanoma, although intrinsic or acquired resistance develops in nearly half of patients. Tumor-infiltrating CD8+ T lymphocytes (TILs) are key determinants of anti-tumor immunity in melanoma and other cancers, and single-cell RNA-sequencing has identified T cell states associated with improved clinical response to ICB, as well as adoptive T cell therapy (ACT). Despite these advances, strategies to identify and analyze tumor-reactive TILs in ICB-resistant patients remain limited. Here, we demonstrate that TILs from ICB-resistant melanoma patients can recognize and eliminate autologous tumor cells independent of class I HLA-TCR interactions. TILs eliminated matched melanoma cells in a time and dose-dependent fashion associated with secretion of effector cytokines. Strikingly, the deletion of B2M (resulting in loss of class I HLA surface expression) did not alter the activity of these TILs. Immunophenotyping studies confirmed that TILs are largely (&amp;gt;95%) effector memory (Tem) CD8 T cells (CD45RA-CD45RO+CCR7-) and give rise to terminal effector cells after co-culture with matched melanoma cells. Further, the elimination of melanoma cells by TILs required intact JAK1/2 signaling, although interferon-gamma (IFNγ) was neither necessary nor sufficient for tumor cell elimination. Together, these findings demonstrate that expanded TILs from ICB-resistant melanoma patients are capable of eliminating melanoma cells via a novel, class I MHC-independent mechanism. Citation Format: Hongyan Xie, Aiping Jiang, Anne Jenney, Yi Sun, Tatyana Sharova, Moshe Sade-Feldman, Or-yam Revach, Angelina Cicerchia, Martin Q. Rasmussen, Nir Hacohen, Robert T. Manguso, Russell W. Jenkins. Class I HLA-independent lysis of immunotherapy-resistant melanoma by CD8 T cells. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4072.
Abstract 1489: Overexpression of ribosomal L22-like1 (RPL22L1) enhances cancer stem cell properties and promotes tumor progression in hepatocellular carcinoma (HCC)
Authors: JinLin Huang, Zhenyang Guo, Jie Luo, Beilei Liu, Lanqi Gong, Xiaona Fang, Dora Lai-wan Kwong, Xinyuan Guan
Journal: Cancer Research (2023)

Abstract The abnormality of ribosomal biogenesis is found to be involved in carcinogenesis and progression in various types of cancer. RPL22L1 gene is a highly homologous paralog of RPL22, both of which belong to the L22E family of ribosomal proteins and take part in encoding a cytoplasmic ribosomal protein that is a component of the 60S subunit of ribosomes. Our previous work has established a CRISPR/Cas9 genome-wide knockout screening profile and developed a two-component mixture model to identify cancer-specific essential genes in 436 cancer cell lines. Bioinformatics analysis reveals that RPL22L1 acts as a feature gene to predict cancer-specific essential genes in human cancer. Previous studies suggest that RPL22L1 is correlated with tumor progression and adverse prognosis in several cancer types, while its role in hepatocellular carcinoma (HCC) is unknown. In the present study, we found that RPL22L1 is highly expressed in embryonic stem cells (ES) and liver progenitor cells, which were acquired from a vitro hepatic differentiation model originating from ES cells. Hence, we hypothesized that RPL22L1 enhances cancer stem cell properties in HCC. Functional assays showed that RPL22L1 would promote sphere formation in vitro. qRT-PCR and Western Blotting results indicated that RPL22L1 could promote stemness-related gene expressions such as CD133, CD44, and SOX9 in HCC cell lines. Overexpression of RPL22L1 increased aldehyde dehydrogenase (ALDH) activity, which was decreased when RPL22L1 was silenced with short hairpin RNA (shRNA). Extreme limiting dilution assay showed that RPL22L1 overexpression stimulated self-renewal ability in immunodeficient mice. Furthermore, higher RPL22L1 expression can also promote cell proliferation and epithelial-mesenchymal-transition, as well as drug resistance to 5-Fluorouracil (5-FU) and cisplatin (CDDP). Silver nitrate staining demonstrated that RPL22L1 overexpression correlated with a larger size and number of nucleoli. The expression of RPL22L1 was also associated with adverse prognosis in HCC clinical samples. In conclusion, our study reveals aberrant ribosomal protein activity in HCC and the role of RPL22L1 in promoting HCC tumor progression. Citation Format: JinLin Huang, Zhenyang Guo, Jie Luo, Beilei Liu, Lanqi Gong, Xiaona Fang, Dora Lai-wan Kwong, Xinyuan Guan. Overexpression of ribosomal L22-like1 (RPL22L1) enhances cancer stem cell properties and promotes tumor progression in hepatocellular carcinoma (HCC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1489.
Abstract 4278: Transcriptome-driven combination design: A computational approach
Authors: Christina Gavazzi, Mikhail G. Grushko, Jeremy M. Goldstein, Mahta Samizadeh, Zakary Y. ElSeht, Katherine Arline
Journal: Cancer Research (2023)

Abstract While rational drug combination design seeks to improve patient outcomes by leveraging biological synergy, outcomes from combination trials often fail. Only 12.4% of phase 1 trials eventually achieve regulatory approval. In vitro evidence has provided the rationale for the drug combinations in 36.6% of phase 1 combination trials, but none of those trials progressed past phase 3 (from a study by Paller CJ et al., 2019). A review of DrugCombDB, a publicly available database of experimental synergy results, supports the idea that such outcomes are mirrored in preclinical data. Analysis of the similarity of synergy scores within indication-specific subsets of cell lines from data in a 2022 paper by Douglass, et al. show that in both breast and lung cancer, combinations predicted to be synergistic in at least one cell line from that indication failed to be synergistic in the majority of indication cell lines more than half of the time. SHEPHERD’s Gene Cluster Voting Algorithm (GCVA) was adapted to improve upon current drug combination design and anticipate transcriptome changes as the result of drug treatment. GCVA relates drug sensitivity to the transcriptome by calculating a list of biomarkers that contribute to sensitivity and resistance for specific drugs, enabling the prediction of drug sensitivity on patient transcriptomic data captured via bulk RNAseq. Via this method, 15,137 publically available synergy data points in the Douglass paper were compared to predictions by our computational approach. For all synergy metrics contained within the dataset (ZIP, Bliss, Loewe, and HSA), the synergistic predictions made by application of GCVA have significantly higher synergy scores compared to the antagonistic predictions made by GCVA (p &amp;lt; .001). These test results support the potential for the application of GCVA to intelligent combination design with the ultimate goal of deployment on a patient-by-patient basis.In addition, analysis of GCVA’s drug efficacy predictions for the cell line DU145 showed that treatment with 9 kinase inhibitors consistently potentiated the predicted efficacy of between 5 and 11 additional drugs. Dasatinib, temsirolimus, and cabozantinib were newly predicted effective after treatment with each and every kinase inhibitor. This data suggests an avenue for the design of intelligent drug combinations based on anticipated transcriptomic changes that may improve on existing synergy prediction methods. Citation Format: Christina Gavazzi, Mikhail G. Grushko, Jeremy M. Goldstein, Mahta Samizadeh, Zakary Y. ElSeht, Katherine Arline. Transcriptome-driven combination design: A computational approach. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4278.